Real-time tracking by IoT and AI has totally revolutionized supply chain management… but today the grail of logistics is prediction.
If having real-time information is essential today, predicting some events is a strategic asset… now within reach. Logistics prediction is now a subject and a real need in the supply chain world, to better manage its resources and operations. But today, is it possible to “see the future”? Without calling on a diviner, how can a supply chain anticipate events?
This is when AI comes into play…..
Already pervasive in our daily lives — on Facebook to create news feeds, in the banking world to detect fraud, or even in the health sector — AI offers enormous potential to companies that adopt it, and transport is no exception. According to the McKinsey & Company consulting firm’s report, logistics is one of the sectors that would benefit most from AI’s contributions: there is a paradigm shift towards predictive and proactive logistics operations, with the aim of optimizing decision-making.
What are the benefits of transport prediction?
Real-time supply chain management is nowadays essential for optimal logistics. The traceability of its flows represents key data for logisticians in order to better manage their operations.
But can we do even better? Real-time visibility is a good thing, but being able to anticipate possible delays or other factors that could slow down your supply chain is likely to improve your logistics management!
Real time makes it possible to manage the daily routine, but prediction makes it possible to be even more proactive, improving operational productivity, but also strategic decision-making.
As we know, logistics is above all a question of costs and deadlines: it is necessary to manage your supply chain well, in order to have the best ROI, by managing fleet and flow management efficiently, the latter constantly evolving according to demand.
Logistics prediction can focus on different trends or contexts that directly impact supply chain operations:
- Demand forecasting, for example – based on history and context – would make it possible to model an inventory and reduce the surpluses mobilized: this is possible thanks to AI, which identifies the demands and the products that sell the most or most quickly are identified. Providing an inventory would also reduce delivery times and thus contribute to better customer satisfaction.
- The prediction of its logistics makes it possible to have a proactive management of unforeseen events and hazards such as the anticipation of ETA, the prediction of asset availability. Predictions of external events are also possible, such as weather conditions, social events (strikes), local disruptions, or predictive maintenance.
- Predictive analytics therefore enable companies to produce actionable insights and thus have proactive decision-making to improve customer satisfaction. This automation of decisions obviously increases the company’s profitability and productivity.
Calculating logistics forecasts and trends has therefore become a real opportunity in this sector, to better orient its operations and thus reduce costs considerably, the main challenges of the Supply Chain of the future.
Why is AI relevant for transport prediction?
However, as we have seen, prediction in transport remains complex. This is due to the amount of data to be taken into account, as well as the high quality required for this data.
However, this trend prediction is possible thanks to AI and Machine Learning. These cognitive computer systems learn about the company and intelligently and effectively identify industry trends and consumer needs that traditional analytics can only identify with difficulty.
The importance of more qualified data
The problem of prediction here is the qualification of data. Indeed, having information on events is possible today, but these data do not always have a perfect qualification.
To be able to enrich these events, it will then be necessary to use several examples or scenarios already experienced.
The difficulty here is to integrate data qualified as context, in order to adjust the desired estimates, a real need for logistics actors today. The AI provides them with this data and thus allows them to contextualize events and unforeseen events, allowing them to be more responsive.
Big Data: too much data to process
Forecasting is possible thanks to AI, and AI is possible thanks to Machine Learning. Machine Learning allows computer systems to learn independently, and to discover patterns. It can then make predictions using a series of examples already experienced (through supervised or unsupervised learning).
We know that one of the performance indicators for transport management is flexibility. With constantly changing flows and demands, forecasting here faces large and complex volumes of data. But simply collecting a significant amount of data is no longer enough to produce a result. Moreover, too much data does not allow for proactive and reactive “human” decision-making.
And that’s where AI is relevant: thanks to Machine Learning, AI has a strong ability to process information from large data volumes. It is therefore easy to forecast your assets according to different scenarios.
To conclude: The AI, soon a must-have in logistics?
AI should lead the new economy that is referred to as the “4th industrial revolution”.
And we can already say that logistics will gain a lot from AI. Today, the IoT represents a real convergence between Big Data and AI. Thanks to AI, the supply chain industry is moving from reactive actions to a proactive and predictive, automated and customized model. The key word of tomorrow’s logistics is to move from analytics to predictive analytics.
This new model allows a better understanding of activities, with a reduction in costs.
Everysens implements transport optimization software via IoT and IA.